Post on 30-Dec-2015
Learning to Associate: HybridBoosted Multi-Target Tracker
for Crowded Scene
Present by 陳群元
Outline
• introduction• Related work• MAP formulation• Affinity model• Results• Conclusion
overview
STAGE 1STAGE 2STAGE 3STAGE 4
Introduction
• learning-based hierarchical approach of multi-target tracking
• HybridBoost algorithm-hybrid loss function
• association of tracklet is formulated as a joint problem of ranking and classification
ranking
• the ranking part aims to rank correct tracklet associations higher than other alternatives
classification
• the classification part is responsible to reject wrong associations when no further association should be done
HybridBoost
• combines the merits of the RankBoost algorithm and the AdaBoost algorithm .
adaboost
RankBoost
Related work
• the earliest works look at a longer period of time in contrast to frame-by-frame tracking.
• To overcome this, a category of Data Association based Tracking algorithm
• there has been no use of machine learning algorithm in building the affinity model.
MAP formulation
• Robust Object Tracking by Hierarchical Association of Detection Responses
• ours
MAP formulation v1
• R = {ri} the set of all detection responses
j j
j j j
i i
i i i
MAP formulation v1(cont.)
• tracklet association
MAP formulation v1(cont.)
MAP formulation v2
MAP formulation v2(cont.)
• Inner cost
• Transition cost
MAP formulation v2(cont.)
• With these ,we can rewrite it
Affinity model
• Hybridboost algorithm• Feature pool and weak learner• Training process
Hybridboost algorithm
• Ie.
T1T2
T3
Hybridboost algorithm(cont.)
Loss function
• initial
Strong ranking classifier
weak
Update weight
Updatesample weight
Update weight
weak weak weak
Hybridboost algorithm
Weak ranking classifier
Feature & threshold
Feature & threshold
Feature & threshold
Feature pool and weak learner
Training process
• T:tracklet set from the previous stage
• G:groundtruth track set
Training process (cont)
• For each Ti T, if∈• connecting Ti’s tail to the head of
some other tracklet
Training process (cont)
• connecting Ti’s head to the tail of some other tracklet before Ti which is also matched to G
Ranking sample set
Binary sample set
Training process (cont.)
• use the groundtruth G and the tracklet set Tk−1 obtained from stage k − 1 to generate ranking and binary classification samples
• learn a strong ranking classifier Hk by the HybridBoost algorithm
• Using Hk as the affinity model to perform association on Tk−1 and generate Tk
Experimental results
• Implementation details• Evaluation metrics• Analysis of the training process• Tracking performance
Implementation details
• dual-threshold strategy to generate short but reliable tracklets
• four stages of association• maximum allowed frame gap 16,
32, 64 and 128• a strong ranking classifier H with
100 weak ranking classifiers• Β=0.75• ζ = 0
Evaluation metrics
track fragments &ID switches
• Traditional ID switch:“two tracks exchanging their ids”.
• ID switch : a tracked trajectory changing its matched GT ID
• track fragments:more strict
compare
Best features
• Motion smoothness (feature type 13 or 14)
• color histogram similarity (feature 4)
• number of miss detected frames in the gap between the two trackelts (feature 7 or 9).
Strong ranking classifier output
Choice of β
Tracking performance
Conclusion and future work
• Use HybridBoost algorithm to learn the affinity model as a joint problem of ranking and classification
• The affinity model is integrated in a hierarchical data association framework to track multiple targets in very crowded scenes.
• The end– Thank you
System Architecture
完成度 項目100% Ground truth data (CAVIAR、 TRECUID08)50% User Interface for ground truth50% Ground truth Learning phase 1、 2、 3、 430% Feature Extraction
0% Dual threshold method0% Input data training phase 1、 2、 3、 4